Increasing the accuracy of prediction improves the performance of photovoltaic systems and alleviates the effects of intermittence on the systems stability. A Nonlinear Autoregressive Network with Exogenous Inputs (NARX) approach was applied to the Vichy-Rolla National Airport's photovoltaic station. The proposed model uses several inputs (e.g. time, day of the year, sky cover, pressure, and wind speed) to predict hourly solar irradiance. Data obtained from the National Solar Radiation Database (NSRDB) was used to conduct simulation experiments. These simulations validate the use of the proposed model for short-term predictions. Results show that the NARX neural network notably outperformed the other models and is better than the linear regression model. The use of additional meteorological variables, particularly sky cover, can further improve the prediction performance.
A. Alzahrani et al., "Predicting Solar Irradiance using Time Series Neural Networks," Procedia Computer Science, vol. 36, pp. 623-628, Elsevier, Nov 2014.
The definitive version is available at https://doi.org/10.1016/j.procs.2014.09.065
Complex Adaptive Systems (2014: Nov. 3-5, Philadelphia, PA)
Electrical and Computer Engineering
Engineering Management and Systems Engineering
Keywords and Phrases
Balloons; Forecasting; Linear Regression; Photovoltaic Cells; Photovoltaic Effects; Regression Analysis; Solar Radiation; System Stability; Time Series; Wind; Adaptive Systems; Linear Regression Models; Meteorological Variables; NARX; Nonlinear Autoregressive Network With Exogenous Inputs; NSRDB; Photovoltaic Systems; Prediction Performance; Short Term Prediction; Complex Networks; Time Series Neural Network
International Standard Serial Number (ISSN)
Article - Conference proceedings
© 2014 Elsevier, All rights reserved.
Creative Commons Licensing
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 3.0 License.
01 Nov 2014